TOP 10 CHALLENGES FOR INVESTMENT BANKS 2016

Reference Data Management: Understanding true cost

Challenge 03

Introduction

In the post-crisis era, firms are struggling to understand the hidden costs
of fundamental changes in the investment banking industry.

As executives strive to meet profitability targets in this new world, they face a constant
drumbeat to reduce costs across the enterprise. In a recent survey of data
management professionals working in capital markets, Accenture and
Greenwich Associates found that the costs and impacts associated with poor
data quality are underestimated at best and sometimes ignored entirely.

Make no mistake: Data quality issues are affecting the top and bottom lines
of firms across the industry. Pre-crisis underinvestment in data quality now
poses a threat to post-crisis control, costs, and future growth and
expansion. High capital ratios, changes in risk-weighted asset levels and
increasingly stringent regulatory reporting rules are profoundly impacting
business fundamentals. Meanwhile, bad data quality is limiting the ability
of banks to act decisively and respond effectively.

Grasping at Straws

DATA MANAGEMENT CHALLENGES: COSTS

Source: Accenture Research, Greenwich Associates

Results from our recent study suggest that data quality is a much larger
issue than you might think.1 Although 70 percent of firms cite data
quality as their biggest day-to-day issue, barely 11 percent track or
measure the cost of bad data. In other words, most firms have no way of
knowing the true impact of bad data on their business performance.

There are many examples of enterprise-wide data initiatives being
abandoned due to cost or insufficient benefit. Throwing money at the
problem has proved unfruitful, yet more than a third of survey respondents are still projecting increases in their data and processing
costs. There seems to be a considerable gap between data quality
issues and potential solutions. It’s time to stop grasping at straws.
No single initiative will solve the problem. A broad, holistic approach
is the answer.

Quality as a business issue

Many initiatives disappoint because they fail to truly comprehend the
problem they are trying to solve. There remains a serious disconnect
between the perceptions of business owners, or data users, and internal
data solution providers. All too often, users are complaining about the
consistency, completeness, structural integrity or functionality of the
enterprise’s data solutions at the same time that internal data providers
are claiming to have built a best-in-class solution.

Just 17 percent of firms that participated in our survey reportedly develop
their data management strategies directly in response to the needs of the
business. Many approach the challenge as simply an IT or operations
issue, completely ignoring the underlying business needs. Others believe
that data quality is a matter of governance, but like other issues in the
investment banking industry, the problem runs much deeper. Data quality
needs to be viewed as an enterprise-wide business challenge impacting all
parts of the firm’s operations. Virtually all calculations performed by the
firm, from capital allocation to regulatory reporting, must have a margin of
error built in to account for bad data.

Results from a Recent Accenture/Greenwich Associates Survey

70%

70% of firms indicate data quality affects costs

11%

11% actively measure the cost of bad data

59%

59% understand the problem but cannot quantify the cost

Source: Accenture Research, Greenwich Associates

Creating Space for a CDO

A chief data officer (CDO) can be the key to success here, assuming the
role is well defined. Virtually every bank has a CDO in place, but there
seems to be no industry-wide consensus on what that role should entail,
with some firms treating it as an IT function and others viewing it as highly
theoretical (setting policy without veering into execution).

Specifically, the CDO should have control of the
entire data lifecycle—one of the primary barriers
to achieving efficiency across the organization.
With those elements in place, the CDO can focus
on:

Governance: Creating an effective control framework
for defining and executing policy throughout the firm.

Business solutions: Identifying strategic and tactical tasks that will drive
business value while improving
data quality.

Rationalization and efficiency: Identifying and acting on opportunities to
improve processes and reduce operational waste.

Innovation: Finding the hidden value in data that has
been plagued by quality issues.

To really affect data
quality, the CDO must be
seen as a business-focused
operational control
function, outside of the IT
hierarchy.

Viewing Data as an Asset

The first step a CDO should undertake is to create a strategy or approach
that is practical, tactical and focused on business challenges. Forty-seven
percent of firms in our survey find it difficult to deliver solutions that can
adapt to the evolving requirements of their businesses, mainly because
their data management strategies are not driven by business needs. Too
often, we see a rush to action, with a focus on potential solutions:
implement this EDM package, plug in this utility or define a firm-wide
taxonomy. To be clear: none of these options is inherently bad, but they all
fail to address the issue at hand.

Rather than trying to address data needs with a one-size-fits-all approach,
careful consideration must be given to the challenges and best practices
in each business vertical. Viewing data as an asset instead of a liability
can help firms manage it appropriately and see new ways to extract its
hidden value.

At this point, the CDO becomes instrumental in driving business priorities
for data solutions and securing sponsorship across the firm. To achieve
measurable business benefits, the enterprise must take concrete steps to
increase data quality instead of embarking on ambitious programs that
fail before completion or struggle to deliver the promised results.

Taking advantage of Every Tool

Beyond hiring a CDO to drive organizational and process change, firms can employ several other innovative tools to help advance their data quality goals.

ANALYTICS

One of the biggest obstacles to improving data quality is finding an effective way to measure it. Most often, firms estimate data quality based on the impact of data errors on their business (e.g., trading errors and regulatory fines). What they really need is an objective way to measure quality that fits within the capabilities of their organization.

Analytics can unlock that metric and shed light on how to improve data procedures. Firms can use analytics to understand which processes use which data, who purchases data and from whom, and where processing bottlenecks exist. Advanced analytics can be incorporated throughout the data management lifecycle to generate data-driven insights for advanced monitoring and running of core operations.

ROBOTICS

When confronted with a data quality challenge, the sheer volume of issues can be daunting— especially when data cleansing costs are fully quantified. The typical solution for repetitive and non-subjective tasks is low-cost business process outsourcing (BPO), but these jobs are prone to error, often require knowledge of in-house processes and/or systems, and are subject to processing peaks and troughs. Investment banks need a scalable, flexible and sustainable solution.

Robotic process automation has the potential to fundamentally change how the world and companies in it operate. Oxford University predicts that 35 percent or more of US jobs could be automated in the next 10 to 20 years.2 Organizations that have already adopted robotic process automation solutions are driving the change, using robotics technology to eliminate human error from key processes and reduce processing costs by up to 80 percent.

IT STARTS AT THE TOP

With the right support, CDOs can be data
champions and catalysts. They can help resolve
data quality issues by driving pragmatic and
cost-effective change in a reference data
program and using that success to build a
culture of quality throughout the organization.

But, it all starts at the top of the house. Chief
operating officers (COOs) at investment banks
can begin to tackle the data challenge by asking
themselves some key questions:

How much did bad data cost our firm last
year? If an answer is not available and a
reliable estimate is not even possible, it is
time to take action.

Do we have a CDO to manage this issue? If
not, the time is now.

Does our CDO have the tools and mandate to
succeed? If not, what steps can we take to
provide that support?

Let’s be clear: The journey to quality data is
long, but the steps mentioned above can help
ensure that firms are well positioned to reach
that goal.

DOMINIC STANYER

CHRIS BRODERSEN

This content has been prepared by Accenture and is for information purposes. No part of this content may be reproduced in any manner without the written permission of Accenture. While we take precautions to ensure that the source and the information we base our judgments on is reliable, we do not represent that this information is accurate or complete and it should not be relied upon as such. It is provided with the understanding that Accenture is not acting in a fiduciary capacity. Opinions expressed herein are subject to change without notice.

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